Now we analyze emmissions per capita

from hashlib import sha1
import altair as alt
import pandas as pd
import numpy as np
from hashlib import sha1
alt.data_transformers.disable_max_rows()

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Cell In[1], line 2
      1 from hashlib import sha1
----> 2 import altair as alt
      3 import pandas as pd
      4 import numpy as np

ModuleNotFoundError: No module named 'altair'

Use read_csv from pandas to load the data from the data folder and assign it to a variable named gapminder_df.

Make sure to parse any time columns using the parse_dates argument.

gapminder_df = pd.read_csv("C:/Users/sindi/Downloads/world-data-gapminder.csv", parse_dates=['year'])
gm_recent_co2 = gapminder_df[gapminder_df["year"]=='2014']
co2_top_40 = gm_recent_co2.nlargest(40, 'co2_per_capita' )

Plot of the worst polluting countries in the world

co2_plot = alt.Chart(co2_top_40).mark_bar().encode(
alt.X("co2_per_capita", title="CO2 Emissions per capita"),
alt.Y("country", title="Country", sort='x'),
alt.Color("region", title='Region')

).properties(title="Worst Polluters")
co2_plot

gm_co2_total = gapminder_df.assign(co2_total=(gapminder_df['co2_per_capita'])*(gapminder_df['population']))
gm_co2_total

	country	year	population	region	sub_region	income_group	life_expectancy	income	children_per_woman	child_mortality	pop_density	co2_per_capita	years_in_school_men	years_in_school_women	co2_total
0	Afghanistan	1800-01-01	3280000	Asia	Southern Asia	Low	28.2	603	7.00	469.0	NaN	NaN	NaN	NaN	NaN
1	Afghanistan	1801-01-01	3280000	Asia	Southern Asia	Low	28.2	603	7.00	469.0	NaN	NaN	NaN	NaN	NaN
2	Afghanistan	1802-01-01	3280000	Asia	Southern Asia	Low	28.2	603	7.00	469.0	NaN	NaN	NaN	NaN	NaN
3	Afghanistan	1803-01-01	3280000	Asia	Southern Asia	Low	28.2	603	7.00	469.0	NaN	NaN	NaN	NaN	NaN
4	Afghanistan	1804-01-01	3280000	Asia	Southern Asia	Low	28.2	603	7.00	469.0	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
38977	Zimbabwe	2014-01-01	15400000	Africa	Sub-Saharan Africa	Low	57.0	1910	3.90	64.3	39.8	0.78	10.9	10.0	12012000.0
38978	Zimbabwe	2015-01-01	15800000	Africa	Sub-Saharan Africa	Low	58.3	1890	3.84	59.9	40.8	NaN	11.1	10.2	NaN
38979	Zimbabwe	2016-01-01	16200000	Africa	Sub-Saharan Africa	Low	59.3	1860	3.76	56.4	41.7	NaN	NaN	NaN	NaN
38980	Zimbabwe	2017-01-01	16500000	Africa	Sub-Saharan Africa	Low	59.8	1910	3.68	56.8	42.7	NaN	NaN	NaN	NaN
38981	Zimbabwe	2018-01-01	16900000	Africa	Sub-Saharan Africa	Low	60.2	1950	3.61	55.5	43.7	NaN	NaN	NaN	NaN

38982 rows × 15 columns

Top 40 CO2 emmiters

co2_total_top_40 = gm_co2_total[gm_co2_total['year']=='2014'].nlargest(40, 'co2_total')
co2_total_top_40.head()

	country	year	population	region	sub_region	income_group	life_expectancy	income	children_per_woman	child_mortality	pop_density	co2_per_capita	years_in_school_men	years_in_school_women	co2_total
7222	China	2014-01-01	1390000000	Asia	Eastern Asia	Upper middle	75.9	12800	1.61	11.5	148.00	7.40	10.9	9.65	1.028600e+10
37225	United States	2014-01-01	318000000	Americas	Northern America	High	78.9	51800	1.95	6.8	34.70	16.50	14.5	14.90	5.247000e+09
15763	India	2014-01-01	1290000000	Asia	Southern Asia	Lower middle	67.8	5390	2.38	47.7	435.00	1.73	8.4	5.66	2.231700e+09
28903	Russia	2014-01-01	144000000	Europe	Eastern Europe	Upper middle	70.8	24900	1.70	8.3	8.78	11.90	13.8	14.50	1.713600e+09
17515	Japan	2014-01-01	128000000	Asia	Eastern Asia	High	83.6	37300	1.43	2.8	352.00	9.47	15.0	15.40	1.212160e+09

Continent comparison

co2_total_plot = (
    alt.Chart(co2_total_top_40)
    .mark_bar()
    .encode(
        alt.X("co2_total", title="Total CO2"),
        alt.Y("country", title="Country", sort="x"),
        alt.Color("region", title="Region"),
    )
    .properties(title="Worst Polluters Total")
)
co2_total_plot

total_co2_plot = alt.Chart(gm_co2_total).mark_area().encode(
alt.X("year", title="Year"),
alt.Y("sum(co2_total)", title="CO2 Emissions"),
alt.Color('region', title="Region")
).properties(title="Total CO2 Emissions")
total_co2_plot

Some of the equations used in the analysis

Here is the sample standard deviation: \begin{equation} \sigma = \sqrt\frac{\sum\limits_{i=1}^{n}(y_i – \bar{y})^2} {n – 1} \end{equation}

Population regression line: \begin{equation} Y_i = \beta_0 + \beta_1 X_i + \epsilon_i \end{equation}

from IPython.display import Image
#Image(filename="jb-book2/image/polluted.png")